Methods and systems for online monitoring using a variable data sampling rate

ABSTRACT

A method for online monitoring of a physical environment using a variable data sampling rate is implemented by a computing device. The method includes sampling, at the computing device, at least one data set using at least one sampling rate. The method also includes processing the at least one data set with condition assessment rules. The method further includes determining whether the at least one data set indicates a change in state of the physical environment. The method additionally includes updating the at least one sampling rate.

BACKGROUND OF THE INVENTION

The Application is a continuation of U.S. patent application Ser. No.13/756,330, filed Jan. 31, 2013, entitled “METHODS AND SYSTEMS FORONLINE MONITORING USING A VARIABLE DATA SAMPLING RATE,” which is herebyincorporated by reference in its entirety.

The field of the invention relates generally to monitoring and samplingdata, and more particularly to methods and systems for use in onlinemonitoring using a variable data sampling rate.

Known methods of monitoring physical environments may include monitoringdata received from physical environments and components of physicalenvironments. Physical environments may include physical machines,physical systems, or combinations thereof. Received data may come in avariety of forms including scalar data, wave form data, and object-baseddata. In some cases, particularly wave form data, receiving such datamay require significant resources, especially computational, storage,and network resources. Further, in some cases a monitored physicalenvironment may have many independent components which are beingmonitored. Thus, in such cases, complete monitoring of all received datamay be a significant burden on the system by straining resourcesincluding computational resources and networking resources.

Many known methods to resolve this complexity involve the use ofsampling methods. In such known methods, sampling involves taking afraction of received data and thereby reducing the burden on systemresources. Such sampling introduces an additional complexity, however,by reducing or impacting the responsiveness of a monitoring system to achange in state of the physical environment. If a sampling rate is setinfrequently, a change in state may be detected slowly and accordingly,responded to slowly.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, a method for online monitoring of a physical environmentusing a variable data sampling rate is provided. The method isimplemented by a computing device. The method includes sampling, at thecomputing device, at least one data set using at least one samplingrate. The method also includes processing the at least one data set withcondition assessment rules. The method further includes determiningwhether the at least one data set indicates a change in state of thephysical environment. The method additionally includes updating the atleast one sampling rate.

In another aspect, a computer-implemented system for online monitoringof a physical environment using a variable data sampling rate isprovided. The system includes a monitoring system capable of monitoringthe physical environment using a plurality of sensors. The system alsoincludes a computing device configured to communicate with the onlinemonitoring system. The computing device includes a processor and amemory device coupled to the processor. The computing device alsoincludes a storage device coupled to the memory device and to theprocessor. The computing device is configured to sample at least onedata set using at least one sampling rate from the online monitoringsystem. The computing device is also configured to process the at leastone data set with condition assessment rules. The computing device isfurther configured to determine whether the at least one data setindicates a change in state of the physical environment. The computingdevice is additionally configured to update the at least one samplingrate.

In another aspect, a computer for online monitoring of a physicalenvironment using a variable data sampling rate is provided. Thecomputer includes a processor and a memory device coupled to theprocessor. The computer also includes a storage device coupled to thememory device and to the processor. The computer is configured to sampleat least one data set using at least one sampling rate from an onlinemonitoring system. The computer is also configured to process the atleast one data set with condition assessment rules. The computer isfurther configured to determine whether the at least one data setindicates a change in state of the physical environment. The computer isadditionally configured to update the at least one sampling rate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary computing device that may beused in a computer-implemented system for online monitoring of aphysical system using a variable data sampling rate;

FIG. 2 is a block diagram of an exemplary computer-implemented systemthat may be used for online monitoring of a physical system using avariable data sampling rate that may include the computing device shownin FIG. 1;

FIG. 3 is a flowchart of an exemplary method that may be implemented tobe used in online monitoring of a physical system using a variable datasampling rate that may use the computer-implemented system shown in FIG.2; and

FIG. 4 is a simplified illustration of data sampled at varying rates bythe computer-implemented system shown in FIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

As used herein, the term “physical system” and related terms, e.g.,“physical systems,” refers to any system composed of one or more partsthat has a physical presence. Physical systems may include, withoutlimitation, vehicles, transportation systems, manufacturing facilities,chemical processing facilities, power generation facilities,infrastructure systems, and communication systems. Physical systems mayalso include, without limitation, complex chemical or biological systemswhere components of such systems may have sensor measurementsassociated.

As used herein, the term “sensor” and related terms, e.g., “sensors,”refers to a devices attached to a monitoring system that may detectcondition data related to a physical system at a given point in time.Also, as used herein, sensors facilitate the detection of condition dataand the transmission of the condition data to a monitoring system.

As used herein, the term “online monitoring” refers to the use of amonitoring system to continuously monitor a physical system. Also, asused herein, online monitoring is used to create the condition data setfrom which the system described herein samples data.

As used herein, the term “condition data” refers to the data detected bythe online monitoring systems through the use of sensors where thecondition data indicate the condition of the physical system. As usedherein, condition data may refer to data including, without limitation,vibration data, thermal data, pressure data, electric data, or any otherdata that may be useful in determining the state of the physical system.Also, as used herein, condition data are typically sampled at a variablesampling rate to yield sampling data.

As used herein, the term “sampling data” refers to any form of data thatmay be detected using an online monitoring system and sampled using themethods and systems described herein. Sampling data may include, withoutlimitation, wave form data, scalar data, vector data, numeric data, orany other data capable of being detected using the sensors in an onlinemonitoring system and sampled using the sampling method and systemdescribed herein.

As used herein, the term “sampling rate” refers to the frequency withwhich continuously monitored data are selected in order to have arepresentative sample of such data. The sampling rate is used in anonline monitoring system to convert condition data to sampling data. Asa sampling rate increases or becomes more frequent, sampling data moreclosely approximates condition data.

FIG. 1 is a block diagram of an exemplary computing device 105 that maybe used in a computer-implemented system for online monitoring of aphysical system using a variable data sampling rate.

Computing device 105 includes a memory device 110 and a processor 115operatively coupled to memory device 110 for executing instructions.Processor 115 may include one or more processing units (e.g., in amulti-core configuration). In some embodiments, executable instructionsare stored in memory device 110. Computing device 105 is configurable toperform one or more operations described herein by programming processor115. For example, processor 115 may be programmed by encoding anoperation as one or more executable instructions and providing theexecutable instructions in memory device 110.

In the exemplary embodiment, memory device 110 is one or more devicesthat enable storage and retrieval of information such as executableinstructions and/or other data. Memory device 110 may include one ormore tangible, non-transitory computer-readable media, such as, withoutlimitation, random access memory (RAM), dynamic random access memory(DRAM), static random access memory (SRAM), a solid state disk, a harddisk, read-only memory (ROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), and/or non-volatile RAM(NVRAM) memory. The above memory types are exemplary only, and are thusnot limiting as to the types of memory usable for storage of a computerprogram.

As used herein, the term “non-transitory computer-readable media” isintended to be representative of any tangible computer-based deviceimplemented in any method or technology for short-term and long-termstorage of information, such as, computer-readable instructions, datastructures, program modules and sub-modules, or other data in anydevice. Therefore, the methods described herein may be encoded asexecutable instructions embodied in a tangible, non-transitory, computerreadable medium, including, without limitation, a storage device and/ora memory device. Such instructions, when executed by a processor, causethe processor to perform at least a portion of the methods describedherein. Moreover, as used herein, the term “non-transitorycomputer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices,including, without limitation, volatile and nonvolatile media, andremovable and non-removable media such as a firmware, physical andvirtual storage, CD-ROMs, DVDs, and any other digital source such as anetwork or the Internet, as well as yet to be developed digital means,with the sole exception being a transitory, propagating signal.

Also, in the exemplary embodiment, memory device 110 includes a sparsedistributed memory (SDM) configuration, wherein such SDM configurationis defined using software. Alternatively, such SDM configuration isdefined using any combination of SDM-capable hardware and SDM-compatiblesoftware that enables online monitoring of a physical system using avariable data sampling rate as described herein.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution bydevices that include, without limitation, mobile devices, clusters,personal computers, workstations, clients, and servers.

Memory device 110 may be configured to store operational measurementsfrom an online monitoring system including, without limitation, waveform data, vector data, numeric data, and/or any other type of data. Insome embodiments, processor 115 removes or “purges” data from memorydevice 110 based on the age of the data. For example, processor 115 mayoverwrite previously recorded and stored data associated with asubsequent time and/or event. In addition, or alternatively, processor115 may remove data that exceeds a predetermined time interval. Also,memory device 110 includes, without limitation, sufficient data,algorithms, and commands to facilitate operation of the onlinemonitoring of a physical system using a variable data sampling rate(discussed further below).

In some embodiments, computing device 105 includes a presentationinterface 120 coupled to processor 115. Presentation interface 120presents information, such as a user interface and/or an alarm, to auser 125. In some embodiments, presentation interface 120 includes oneor more display devices.

In some embodiments, computing device 105 includes a user inputinterface 130. In the exemplary embodiment, user input interface 130 iscoupled to processor 115 and receives input from user 125. User inputinterface 130 may include, for example, a keyboard, a pointing device, amouse, a stylus, a touch sensitive panel (e.g., including, withoutlimitation, a touch pad or a touch screen), and/or an audio inputinterface (e.g., including, without limitation, a microphone). A singlecomponent, such as a touch screen, may function as both a display deviceof presentation interface 120 and user input interface 130.

A communication interface 135 is coupled to processor 115 and isconfigured to be coupled in communication with one or more otherdevices, such as a sensor or another computing device 105, and toperform input and output operations with respect to such devices. Forexample, communication interface 135 may include, without limitation, awired network adapter, a wireless network adapter, a mobiletelecommunications adapter, a serial communication adapter, and/or aparallel communication adapter. Communication interface 135 may receivedata from and/or transmit data to one or more remote devices.

Presentation interface 120 and/or communication interface 135 are bothcapable of providing information suitable for use with the methodsdescribed herein (e.g., to user 125 or another device). Accordingly,presentation interface 120 and communication interface 135 may bereferred to as output devices. Similarly, user input interface 130 andcommunication interface 135 are capable of receiving informationsuitable for use with the methods described herein and may be referredto as input devices.

FIG. 2 is a block diagram of an exemplary computer-implemented system200 that may be used for online monitoring of a physical system 220using a variable data sampling rate that may include computing device105 (shown in FIG. 1). Computer-implemented system 200 includes amonitoring system 210 capable of monitoring physical system 220 using aplurality of sensors 215. In the exemplary embodiment, monitoring system210 is a condition monitoring system capable of monitoring physicalsystem 220. Monitoring system 210 is capable of diagnostics, alerts, andcoordinating maintenance of problems in physical system 220. Inalternative embodiments, monitoring system 210 may include additionalattributes or subsets of these capabilities. In the exemplaryembodiment, physical system 220 is a hydro turbine generator. Inalternative embodiments, physical system may include, withoutlimitation, wind turbines, electrical equipment, reciprocatingcompressors, turbomachinery, oil and/or gas refineries, petroleumprocessing systems, or any other physical system capable of beingmonitored by computer-implemented system 200.

Computer-implemented system 200 also includes a computing device 105capable of communicating with monitoring system 210. In the exemplaryembodiment, computing device 105 and monitoring system 210 are distinct.In alternative embodiments, computing device 105 and monitoring system210 may be co-resident on the same computing device. In otherembodiments, monitoring system 210 may represent multiple physicalmonitoring systems. Computing device 105 includes processor 115 andmemory device 110. Computing device 105 also includes storage device235. Storage device 235 is coupled to processor 115 and to memory device110. Storage device 235 is configured to be capable of storing receiveddata including, without limitation, data described below.

In operation, monitoring system 210 detects condition data from sensors215. In the exemplary embodiment, sensors 215 include a first sensor 217and a second sensor 219. In alternative embodiments, sensors 215 mayinclude any number of sensors. In the exemplary embodiment, first sensor217 and second sensor 219 detect data from different physical sectionsof physical system 220. Monitoring system 210 detects condition datafrom each sensor distinctly. In alternative embodiments, sensors 215 maydetect data from the same sections or different sections of physicalsystem 220 and may generate similar types of data or divergent types ofdata. In the exemplary embodiment, monitoring system 210 detects firstcondition data 240 from first sensor 217 and second condition data 245from second sensor 219. In the exemplary embodiment, first conditiondata 240 and second condition data 245 are both waveform data. Waveformdata can consume significant resources in terms of network bandwidth,storage, and memory. Because of this, monitoring system 210 samples dataat a particular sampling rate to generate sampled data.

Sampled data represent a “snapshot” of condition data at a point in timeand each “snapshot” is separated by an interval corresponding to thesampling rate. In the exemplary embodiment, first condition data 240 andsecond condition data 245 are sampled at distinct sampling rates. Inalternative embodiments, they may be sampled at identical samplingrates. Sampling rates may be at any interval of time including, withoutlimitation, a number of hours, a number of minutes, a number of seconds,a number of milliseconds, a number of microseconds, and a number ofnanoseconds. Monitoring system 210 transmits sampled data as firstsampled data 250 corresponding to first condition data 240 and secondsampled data 255 corresponding to second condition data 245 to computingdevice 105.

Computing device 105 receives first sampled data 250 and second sampleddata 255 through a data network (not shown). The data network mayinclude, without limitation, local area networks (LAN), wirelessnetworks, wide area networks (WAN), backbone networks, or any other kindof network capable of transmitting and receiving information describedin this system. Computing device 105 is capable of processing data atprocessor 115 to determine sampling rates. In the exemplary embodiment,processor 115 initially defines unique schedules for each sampled data.For example, in the exemplary embodiment, processor 115 will set asampling rate and schedule for first condition data 240 and secondcondition data 245 so that first condition data 240 are minimallysampled at the same time as second condition data 245. This schedulingminimizes the impact on system resources including, without limitation,bandwidth, processing, and storage. Processor 115 determines uniqueschedules for each sampled data using distinct-prime factorizationmethods. These methods use distinct-prime numbers as factors of samplingrates. Because prime factors are inherently unique, this allows fordistinct sampling rates. In most cases this will ensure that samplingwill not overlap. However, overlapping may become necessary even whenapplying such methods if sufficient numbers of waveform data and/orfrequency of sampling are required.

Computing device 105 is also capable of changing the sampling rates ofsampling using processor 115 by applying condition assessment rules.Computing device 105 initially receives sampled data (e.g., firstsampled data 250 and second sampled data 255) at a given sampling ratedetermined by the distinct-prime factorization method described above.Computing device 105 also includes condition assessment rules which canbe run against sampled data. Condition assessment rules may be stored onmemory device 110, storage device 235, external storage (not shown), ormay be input by a user (not shown in FIG. 2). Condition assessment rulesare used to determine whether a change in state has occurred in physicalsystem 220 based upon sampled data. Condition assessment rules areexecuted by processor 115 applying the rules to sampled data. Whencondition assessment rules are executed by processor 115, processor 115will determine a new sampling rate that updates the previous samplingrate for each sampled data. Updating the rate may include increasing thesampling rate, reducing the sampling rate, or holding the sampling ratesubstantially constant. For example, the rate may be increased ifphysical system 220 is determined by the condition assessment rules tohave entered an anomalous state. In such cases, there is value inincreasing the frequency of monitoring to assess the severity orvalidity of the anomalous state. Increased frequency of monitoring,caused by the increase in sampling rate, can allow for system 200 andthe individuals who maintain physical system 220 to be more responsiveto diagnostic, maintenance, or other issues created by a potentialanomalous condition. The rate may be decreased if physical system 220 isdetermined by the condition assessment rules to have left an anomalousstate. In such a situation, once system 200 has validated that physicalsystem 220 is no longer anomalous, resource costs of more frequentsampling may no longer be justified. Alternately, the rate may beincreased if physical system 220 is determined by the conditionassessment rules to have left an anomalous state. In such a situation,it may be valuable to quickly validate that physical system 220 hasactually left an anomalous state and resources used by increasedsampling will be justified. The rate may also be decreased if physicalsystem 220 is determined by the condition assessment rules to be in ananomalous condition but such condition has been confirmed. In caseswhere diagnostics or maintenance may be time consuming, it may no longerbe valuable to continue to monitor physical system 220 heavily if itscondition of anomaly is already known. The rate may be left unchanged ifphysical system 220 has not changed based upon the application ofcondition assessment rules.

In some embodiments, a threshold setting for a minimum time thresholdbetween changes in physical state may be defined. The minimum timethreshold may define the minimum time that must pass between twodeterminations of a change in state. The minimum time threshold may bestored in computing device 105 at memory device 110 or storage device235. In these embodiments, condition assessment rules may indicate thata change in state of physical system 220 has occurred previously and isnow occurring again but a change in the sampling rate will not occur.This threshold may be defined in situations where a system has recentlybeen repaired and is recovering or where data may be unreliable. Thethreshold may alternatively be defined when a change in state has beenpreviously reported, maintenance has not been scheduled for an extendedperiod of time, and there is no reason to repeat the event and therebyincrease the sampling rate. This may occur, for example, when the changein state is from a normal state to a low severity level anomaly state.

In the exemplary embodiment, in addition to changing the sampling rateafter a change in state of physical system 220 is determined, computingdevice 105 may record the change in state as a historical event. Thehistorical event may be saved at memory device 110, storage device 235,or on external storage. In these embodiments, computing device 105 willalso transmit the change in state to an external system including, forexample, monitoring system 210. Monitoring system 210 may recordhistorical detections of changes in state and sampling rates inconjunction with other monitoring records. In some embodiments, despitea change in state of physical system 220, computing device 105 willdelay at least one of updating the sampling rate and transmitting thechange in state to an external system. A delay may be made for severalreasons. First, updating the sampling rate may be delayed to avoidsimultaneous sampling from different components of the physical system.Due to the network and system resources discussed above, it may beadvantageous to avoid such simultaneous sampling. Second, a delay mayoccur if a machine in physical system 220 is expected to go through asequence of known states where rapid sampling would be inappropriate.Transmitting the change in state may be delayed because it may bevaluable to wait longer to confirm the change in state of physicalsystem 220, to avoid redundant data, or to minimize resource overhead.Delaying may occur based upon a threshold waiting period defined by auser, computing device 105 applying heuristic algorithms, or externallyreceived data.

FIG. 3 is a flowchart of an exemplary method 300 that may be implementedto be used in online monitoring of physical system 220 using a variabledata sampling rate using computer-implemented system 200 (shown in FIG.2). Method 300 is executed at processor 115 in computing device 105.Processor 115 samples 305 at least one data set scheduled for samplingusing at least one sampling rate. Sampling 305 represents receivingsampled data (e.g., first sampled data 250 and second sampled data 255)from monitoring system 210 where sampled data represents data sampledfrom condition data using at least one sampling rate. Processor 115 alsoprocesses 315 at least one data set with condition assessment rules.Processing 315 at least one data set with condition assessment rulesrepresents applying condition assessment rules stored in memory device110 or storage device 235 on the sampled data. Processor 115 furtherdetermines 325 whether the at least one data set indicates a change instate of the physical environment. Determining 325 whether the at leastone data set indicates a change in state of the physical environmentrepresents processor 115 identifying whether a change in state ofphysical system 220 has occurred. Processor 115 finally updates 335 theat least one sampling rate. Updating 335 the at least one sampling raterepresents increasing, reducing, or holding constant the sampling rate.In some embodiments, updating 335 the at least one sampling rate alsorepresents changing sampling rate for a first condition data 240 so thatfirst sampled data 250 is not sampled at the same time as second sampleddata 255.

FIG. 4 is a simplified illustration 400 of data sampled at varying ratesby computer-implemented system 200 (shown in FIG. 2). Illustration 400displays a graph indicating sampled data over a time period. Thevertical axis 405, labeled “Extracted Characteristic” reflects a scalarvalue associated with waveform data. Therefore, as there aretwenty-three values for “Extracted Characteristic” 405, this reflectstwenty-three waveforms which were sampled and from which twenty-three“Extracted Characteristic” 405 values were determined. The label“Extracted Characteristic” 405 reflects the isolation of the values fromthe sampled waveform data that are of interest in determining thecondition of physical system 220. In alternative embodiments, “ExtractedCharacteristic” 405 may not be representable by a scalar value andinstead may require multidimensional analysis or other complex analysis.Illustration 400 presents particular values of “ExtractedCharacteristic” 405 presented over the horizontal axis 410 representingtime. Illustration 400 presents values 420 of “Extracted Characteristic”405 for points in time 410.

Illustration 400 demonstrates the application of condition assessmentrules. Initially “Extracted Characteristic” 405 values 420 are sampledat a first sampling rate 425. On the fourth data point of “ExtractedCharacteristic” 405 values 420, “Extracted Characteristic” 405 exceeds athreshold value 435 indicated by the solid line. At this point, thesampling rate is changed to a second sampling rate 430 because of afirst condition assessment rule 431. Condition assessment rule 431 hasidentified a potential change in physical system 220 because of thevalue for “Extracted Characteristic” 405 exceeding threshold value 435and accordingly changes sampling rate to second sampling rate 430.

A second condition assessment rule 440 is now invoked to confirm thepresence of a change in physical system 220. Rule 440 represents acondition assessment rule for a Severity 3 or Sev 3 Event. The Severity3 event is used when there is confirmation that physical system 220 haschanged. Rule 440 defines a Severity 3 event as four consecutive“Extracted Characteristic” 405 values 420 exceeding threshold value 435.Although the fourth “Extracted Characteristic” 405 value 420 exceeds theline threshold value 435 associated with rule 440, a Sev 3 Event is nottriggered until the ninth “Extracted Characteristic” 405 value 420. Whensecond condition assessment rule 440 is satisfied, the sampling ratemoves from second sampling rate 430 back to first sampling rate 425because there is no need for frequent sampling.

A third condition assessment rule 433 is invoked on the sixteenth“Extracted Characteristic” 405 value 420 when “Extracted Characteristic”drops back below reset threshold value 455. At this point, the samplingrate is changed back to second sampling rate 430 because third conditionassessment rule 433 is satisfied. Condition assessment rule 433 hasidentified a new potential change in physical system 220 because of thevalue for “Extracted Characteristic” 405 has fallen below resetthreshold value 455.

A fourth condition assessment rule 450 is now invoked to confirm thepresence of a change in physical system 220. Rule 450 represents acondition assessment rule for a reset or end of a Severity 3 or Sev 3Event. The reset of Severity 3 event is confirmed when there isconfirmation that physical system 220 has changed back to a normalstate. Rule 450 defines a Severity 3 reset event as three consecutive“Extracted Characteristic” 405 values 420 falling below reset thresholdvalue 455. Although the sixteenth “Extracted Characteristic” 405 value420 falls below reset threshold 455, a Sev 3 Event reset is nottriggered until the twenty-first “Extracted Characteristic” 405 value420. When fourth condition assessment rule 450 is satisfied, thesampling rate changes from second sampling rate 430 back to firstsampling rate 425 because there is no need for frequent sampling.

In contrast to known, conventional systems and methods for onlinemonitoring, the systems and methods as described herein facilitateincreasing the efficiency and responsiveness of the online monitoring ofa physical system. Also, such systems and methods facilitate reducingthe cost of monitoring a physical system. Further, such systems andmethods facilitate improving the monitoring of the physical system byselectively altering sampling rates at relevant times.

An exemplary technical effect of the methods, systems, and apparatusdescribed herein includes at least one of: (a) improving the resourceutilization of online monitoring systems through effective scheduling ofsampling that is responsive to conditions and interdependencies; (b)improving responsiveness of online monitoring systems to changes inconditions by selectively altering sampling rates; (c) improving themaintenance of physical systems by efficient and responsive monitoring;and (d) reducing redundant identifications of changes in state ofphysical system.

The methods and systems described herein are not limited to the specificembodiments described herein. For example, components of each systemand/or steps of each method may be used and/or practiced independentlyand separately from other components and/or steps described herein. Inaddition, each component and/or step may also be used and/or practicedwith other assemblies and methods.

Some embodiments involve the use of one or more electronic or computingdevices. Such devices typically include a processor or controller, suchas a general purpose central processing unit (CPU), a graphicsprocessing unit (GPU), a microcontroller, a reduced instruction setcomputer (RISC) processor, an application specific integrated circuit(ASIC), a programmable logic circuit (PLC), and/or any other circuit orprocessor capable of executing the functions described herein. Themethods described herein may be encoded as executable instructionsembodied in a computer readable medium, including, without limitation, astorage device and/or a memory device. Such instructions, when executedby a processor, cause the processor to perform at least a portion of themethods described herein. The above examples are exemplary only, andthus are not intended to limit in any way the definition and/or meaningof the term processor.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

1-20. (canceled)
 21. A method for continuous monitoring of a physicalenvironment using a variable data sampling rate, said method implementedby a computing device, said method comprising: receiving at least onedata set of condition data from at least one sensor coupled to thephysical environment; determining a schedule to sample the at least onedata set of condition data, wherein the schedule is determined usingdistinct prime-factorization to schedule each data set sampling at adistinct point in time; sampling the at least one data set of conditiondata using at least one sampling rate and based on the determinedschedule, wherein said computing device includes a sparse distributedmemory configuration; applying at least one condition assessment rule tothe at least one data set of condition data; determining whether said atleast one data set of condition data indicates a change in state of thephysical environment; updating said at least one sampling rate; andoutputting at least one modified data set based on the updated samplerate, the outputting including providing the at least one modified dataset via presentation interface of the computing device.
 22. The methodof claim 21, wherein said at least one data set of condition data issampled from a continuous monitoring system, the continuous monitoringsystem is at least one of: an external monitoring system; a plurality ofexternal monitoring systems; and said computing device.
 23. The methodof claim 21, wherein said condition data includes vibration data,thermal data, pressure data, electric data, or state data associatedwith the physical environment.
 24. The method of claim 21, whereinupdating said at least one sampling rate comprises at least one of:increasing the at least one sampling rate; reducing the at least onesampling rate; and holding the at least one sampling rate substantiallyconstant.
 25. The method of claim 24, wherein updating said at least onesampling rate further comprises: determining whether a change in statehas been previously identified; and updating said at least one samplingrate only if the change in state has been previously identified outsidea defined threshold interval.
 26. The method of claim 21, whereindetermining whether said at least one data set of condition dataindicates a change in state of the physical environment comprises:storing the change in state of the physical environment in at least oneof a memory device, a storage device, and an external storage device;and transmitting the change in state to an external system.
 27. Themethod of claim 26, further comprising delaying at least one of:updating said at least one sampling rate; and transmitting the change instate to an external system, based upon thresholds including at leastone of: user defined thresholds; machine learning thresholds; andexternally received data.
 28. A computer-implemented system forcontinuous monitoring of a physical environment using a variable datasampling rate comprising: an continuous monitoring system capable ofmonitoring the physical environment using a plurality of sensors; and acomputing device configured to communicate with said continuousmonitoring system including a processor, a memory device coupled to saidprocessor and including a sparse distributed memory configuration, and astorage device coupled to said memory device and to said processor, saidcomputing device configured to: receive at least one data set ofcondition data from at least one sensor of the plurality of sensorscoupled to the physical environment; determine a schedule to sample theat least one data set of condition data, wherein the schedule isdetermined using distinct prime-factorization to schedule each data setsampling at a distinct point in time; sample at least one data set ofcondition data using at least one sampling rate from said continuousmonitoring system and based on the determined schedule; apply at leastone condition assessment rule to the at least one data set of conditiondata; determine whether the at least one data set of condition dataindicates a change in state of the physical environment; update said atleast one sampling rate; and output at least one modified data set basedon the updated sample rate, the outputting including providing the atleast one modified data set via presentation interface of the computingdevice.
 29. The computer-implemented system of claim 28, wherein saidcontinuous monitoring system further comprises at least one of: anexternal monitoring system; a plurality of external monitoring systems;and said computing device.
 30. The computer-implemented system of claim28, wherein said condition data includes vibration data, thermal data,pressure data, electric data, or state data associated with the physicalenvironment.
 31. The computer-implemented system of claim 28, whereinthe computing device is further configured to at least one of: increasesaid at least one sampling rate; reduce said at least one sampling rate;and hold said at least one sampling rate substantially constant.
 32. Thecomputer-implemented system of claim 31, wherein the computing device isfurther configured to: determine whether a change in state has beenpreviously identified; and update said at least one sampling rate onlyif the change in state has been previously identified outside a definedthreshold interval.
 33. The computer-implemented system of claim 28,wherein the computing device is further configured to: store the changein state of the physical environment in at least one of a memory device,a storage device, and an external storage device; and transmit thechange in state to an external system.
 34. The computer-implementedsystem of claim 33, wherein the computing device is further configuredto delay at least one of: updating said at least one sampling rate; andtransmitting the change in state to an external system based uponthresholds including at least one of: user defined thresholds; machinelearning thresholds; and externally received data.
 35. A computer forcontinuous monitoring of a physical environment using a variable datasampling rate comprising: a processor; a memory device coupled to saidprocessor and including a sparse distributed memory configuration; and astorage device coupled to said memory device and to said processor, saidcomputer configured to: receive at least one data set of condition datafrom at least one sensor of the plurality of sensors coupled to thephysical environment; determine a schedule to sample the at least onedata set of condition data, wherein the schedule is determined usingdistinct prime-factorization to schedule each data set sampling at adistinct point in time; sample at least one data set of condition datausing at least one sampling rate from a continuous monitoring system andbased on the determined schedule; apply at least one conditionassessment rule to the at least one data set of condition data;determine whether the at least one data set of condition data indicatesa change in state of the physical environment; update said at least onesampling rate; and output at least one modified data set based on theupdated sample rate, the outputting including providing the at least onemodified data set via presentation interface of the computing device.36. The computer of claim 35, wherein said condition data includesvibration data, thermal data, pressure data, electric data, or statedata associated with the physical environment.
 37. The computer of claim35, wherein said computer configured to update said at least onesampling rate is configured to at least one of: increase said at leastone sampling rate; reduce said at least one sampling rate; and hold saidat least one sampling rate substantially constant.
 38. The computer ofclaim 37, wherein said computer configured to update said at least onesampling rate is further configured to: determine whether a change instate has been previously identified; and update said at least onesampling rate only if the change in state has been previously identifiedoutside a defined threshold interval.
 39. The computer of claim 35,wherein the computer configured to determine whether the at least onedata set of condition data indicates a change in state of the physicalenvironment is further configured to: store the change in state of thephysical environment in at least one of a memory device, a storagedevice, and an external storage device; and transmit the change in stateto an external system.
 40. The computer of claim 39, wherein thecomputer configured to determine whether the at least one data set ofcondition data indicates a change in state of the physical environmentis further configured to delay at least one of: updating said at leastone sampling rate; and delay transmitting the change in state to anexternal system based upon thresholds including at least one of: userdefined thresholds; machine learning thresholds; and externally receiveddata.